Remote Monitoring Technology for COVID-19 Patients

Human beings have adapted to a new way of life, from an open society to a closed world. Covid19 has brought many changes to the way people live, social distance and minimal to no contact are our goals. This could have been impossible if we were not surrounded by technology. This Perspective offers a context for the implementation of Remote Technologies, demonstrating the forms in which pandemic preparation, monitoring, research, tracking, and Health Care Technologies are effectively implemented. Our research is focused on providing a solution for handling patients at health facilities with the help of remote technologies keeping in mind the importance of social distancing and minimum to no contact to minimize the spread of the virus. The strategy is to identify the two fronts where contact with a patient is to be made. The first front is the point at which a patient needs to be detected and classified as a potential risk, at this stage the technology needs to identify the patient's symptoms, gather relevant data, and help doctors build a portfolio. The second front is where an admitted patient needs to be monitored and taken care of with regular check-ups, the technology should also take good of the patient’s mental health. The paper proposes a solution to device remote technologies that could prove beneficial in the fight against the deadly coronavirus.

Improving Power Quality and Mitigation of Harmonic Distortion Impact at Photovoltaic Electric Vehicle Charging System

This article offers a clear and realistic design for an active power filter to increase reliability and power quality of the photovoltaic charging system and a high-penetration electric vehicle distribution system. The MOPSO algorithm is used as the basis for problems with optimization and filter tuning. A typical regular load curve is used to model the warped power grid over a 24-hour cycle to estimate the total harmonic distortion (THD). For structures with high penetration of electric cars, the probability of minimizing THD (for example to five percent) is explored via optimum capacity active shunt filters and shunt capacitors. To maximize general performance of the charging system, the switching systems are re-scheduled. Moreover, to increase the current control accuracy of shunt active filter, the fuzzy logic controller is utilized. The major drawback to new system is that it would have unrestricted billing for entire day to cope with voltage interruption. In MATLAB / SIMULINK, detailed machine setup and control algorithm experiments are simulated. The simulation findings confirm the efficiency and viability of projected shunt active filter to enhance voltage profile and track power performance of photovoltaic charging system.

E-Notice Board (ENB) for the Faculty Community

This research work developed Electronic Notice Board (ENB) for the faculty community. The purpose is to upgrade the already existing manual method of information dissemination, so as to improve the administrative work of the faculty, and to create an enabling environment for more efficient and friendly means of information delivery. It will improve the rate at which staff and students participate in faculty events and activities. The conventional notice board is one of the oldest methods used in information delivery and announcements to students. Students go to where the notice board is mounted in other to read updates or announcements. Use of wall notice board in every place is a tedious work since it needs to be updated regularly and with correct and right information manually. Few of the problems that necessitated the need for ENB are; inadequate time to read all the relevant information pasted newly on a notice board as a result of tight schedule since the copies are limited. Limited time-lag of newly pasted notice since people mutilate, remove or destroy the paper notices from the board leaving others uninformed. Some of the objectives are to enable individuals to view the update from their various devices from any location. To enable instant update to all users since notice is online. To allow individuals download and store copies of original information intact. Generally, it looks at the existing faculty notice boards, building a system that makes it run by the internet access or by local area network (LAN) so as to increase the rate at which relevant information is being disseminated to the faculty community with no location restriction. The user is kept updated each time the E-Notice Board is updated based on their categories through an SMS alert. This system intends to simplify and improve the University faculties to perform their daily activities since most of the school organs uses computerized system. This research work was designed using Object Oriented Analysis and Design Methodology (OOADM) and implemented using Hypertext Pre-Processor (PHP), Hypertext Markup Language (HTML), Bootstrap, Cascading Style Sheet (CSS) as front-end and My Structural Query Language (MYSQL) database as back-end.

Efficient Pneumonia Detection for Chest Radiography Using ResNet-Based SVM

Chest radiography has a significant clinical utility in the medical imaging diagnosis, as it is one of the most basic examination tools. Pneumonia is a common infection that rapidly affects human lung areas. So, finding an advanced automated method to detect Pneumonia is assigned to be one of the most recent issues, which is still prohibitively expensive to mass adoption, especially in the developing countries. This article presents an innovative approach for distinguishing the residence of pneumonia by embedding computational techniques to chest x-rays images which eliminating the demands for single-image investigation and significantly decrease the total costs. Recent advances in deep learning achieved remarkable results in image classification on different domains; however, its application for Pneumonia diagnosis is still restricted. Hence, the main focus is to provide an investigation that will improve the research in this area, presenting a new proposal to the applications of pre-trained convolutional neural networks (CNNs) as a stage of features extraction to detect this disease. Specifically, we propose to combine deep residual neural networks (ResNets), which extract the hierarchical features from the individual x-ray images with the boosting algorithm to select the salient features, and support vector machine for classification (AdaBoost-SVM). After conducting the performance analysis on the available dataset, we have concluded that the precision of the introduced scheme in Pneumonia classification is superior to the most concurrent approaches, resulting in a great improvement in clinical outcomes.

EEG Channel Selection Using A Modified Grey Wolf Optimizer

Consider an increasingly growing field of research, Brain-Computer Interface (BCI) is to form a direct channel of communication between a computer and the brain. However, extracting features of random time-varying EEG signals and their classification is a major challenge that faces current BCI. This paper proposes a modified grey wolf optimizer (MGWO) that can select optimal EEG channels to be used in (BCIs), the way that identifies main features and the immaterial ones from that dataset and the complexity to be removed. This allows (MGWO) to opt for optimal EEG channels as well as helping machine learning classification in its tasks when doing training to the classifier with the dataset. (MGWO), which imitates the grey wolves leadership and hunting manner nature and which consider metaheuristics swarm intelligence algorithms, is an integration with two modification to achieve the balance between exploration and exploitation the first modification applies exponential change for the number of iterations to increase search space accordingly exploitation, the second modification is the crossover operation that is used to increase the diversity of the population and enhance exploitation capability. Experimental results use four different EEG datasets BCI Competition IV- dataset 2a, BCI Competition IV- data set III, BCI Competition II data set III, and EEG Eye State from UCI Machine Learning Repository to evaluate the quality and effectiveness of the (MGWO). A cross-validation method is used to measure the stability of the (MGWO).

Prediction of Interpolants in Zero Diluted Images

This paper provides an algorithmic procedure to predict interpolants of zero diluted images. Given a digital image, one can zero dilute it by right adjoining a column consisting of ‘0s’ to every column except the last column and inserting a row consisting of ‘0s’ below every row except the last row. This yields a new image with a size (2W-1)×(2H-1), where W is the width and H is the height of the original image. Another way of zero diluting an image is by right adjoining a column consisting of ‘0s’ to every column and inserting a row consisting of ‘0s’ below every row. This yields a new image with a size (2W)×(2H), where W is the width and H is the height of the original image. Alternatively, subsampling of an image is carried out by forcing pixel values in the alternate columns and rows to zero. Thus, the size of the subsampled image is reduced to half of the size of the original image. This means 75% of the information in the original image is lost in the subsampled image. On the other hand, zero dilution of an image does not cause loss of information but increases the possibility of predicting more information. The question that arises here is whether it is possible to predict more pixel values, which are called interpolants so that the reconstructed image is an enhanced version of the original image in resolution. In this paper, two novel interpolant prediction techniques, which are reliable and computationally efficient, are discussed. They are (i) interpolant prediction using neighborhood pixel value averaging and (ii) interpolant prediction using extended morphological filtering. These techniques can be applied to predict interpolants in a subsampled image also.

Research on Tensor-Based Cooperative and Competitive in Multi-Agent Reinforcement Learning

As technology overgrows, the assortment of information and the density of work becomes demanding to manage. To resolve the density of employment and human labor, machine-learning (ML) technology developed. Reinforcement learning (RL) is the recent advancement of ML studies. Multi-agent reinforcement learning (MARL) is useful to train multiple agents in the surrounding environment. The previous research studies focused on two-agent cooperation. Their data representation was held in a two-dimensional array, which is called a matrix. The limitation of this two-dimensional array appears as the training data of agents increases. The growth in the training data of agents creates storage drawbacks and data redundancy. Our first aim in this research is to improve an algorithm that can represent MARL training in tensor. In MARL, multiple agents are work together to achieve joint work. To share the training records and data of numerous agents, we need to collect the previous cumulative experience of agents in tensor. Secondly, we will discover the agent's cooperation and competition, with local and global goals of agents in MARL. Local goals are the cooperation of agents in a group or team where we use the training model as a student and teacher agent. The global goal is the competition between two contrary teams to acquire the reward. All learning agents have their Q table for storing the individual agent's training data in an environment. The growth in the number of learning agents, their training experience in Q tables, and the requirement for representing multiple data become the most challenging issue. We introduce tensor to store various data to resolve the challenges for data representation in multiple agent associations. Tensor is expressed as the three-dimensional array, although it is an N-way array, which is useful for representing and accessing numerous data. Finally, we will implement an algorithm for learning three cooperative agents against the opposed team using a tensor-based framework in the Q learning algorithm. We will provide an algorithm that can store the training records and data of multiple agents. Tensor advances to get a small storage size than the matrix for the training records of agents. Although three agent cooperation benefits to having maximum optimal reward.

Design of an Ultra-Low Powered Data-Logger for Stand-Alone PV Energy Systems

This paper presents an open-source, ultra-low powered data-logger for off-grid photovoltaic (PV) energy systems. Deep-sleep mode of ESP32-S2 microcontroller is used along with voltage, current, and light sensors for logging the data of PV energy system to an external micro SD card. A toggle switch is used to switch the operational modes of data-logger between deep-sleep and web-server modes. Real-time PV data can be monitored in a local web-portal programmed in the microcontroller. The same web-portal is also used to check and download the historical data of a PV energy system. The energy consumption of the designed system is 7.33mWh during deep-sleep mode and 425mWh during the web-server mode. The total cost of the designed data-logger is C$ 30.

A Review of Conventional Fault Detection Techniques in Solar PV Systems and a Proposal of Long Range (LoRa) Wireless Sensor Network for Module Level Monitoring and Fault Diagnosis in Large Solar PV Farms

This paper reviews various faults that exist in large solar Photovoltaic (PV) systems. The faults are reviewed in their various classes based on the location and structure. Conventional solutions for fault detection and various research work in PV system monitoring and fault detection are reviewed. It is obvious that PV module level monitoring exhibit advantages over array or string monitoring. Therefore, the paper proposes the use of Long Range (LoRa) Wireless Sensor Networks (WSN) for PV module level monitoring and fault diagnosis. LoRa was proposed for this application due to the advantages it has over other wireless technologies which include long range of data transfer, low cost, low power consumption and multi sensor connection capabilities.

Dynamic Modeling and Control of a Single-Phase Grid-Connected photovoltaic System

Designing control strategies to connect a photovoltaic (PV) system to the grid has been significantly challenging. This paper focuses on developing a controller for a single-phase PV system connected to the grid and its implementation to modify the power factor in the distribution power system. To design a grid-connected PV system, its components are modeled, such as PV panels, Maximum Power Point tracking (MPPT) algorithm, the grid interface inverter with the appropriate filter, and the DC link capacitor. SIMULINK / MATLAB is used for simulation in this study. The proposed control strategy is designed to track the maximum power point of The PV panels and control the PV active and reactive output power. In this paper, the presented reactive power control provides the PV system with power factor correction (PFC) capability. The proposed technique for checking controller validity is tested, and the results prove that the proposed controller is good and provides the required performance.